{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e53d0ab-738c-40b0-a080-fd286a84f6cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
      "Requirement already satisfied: numpy in /root/miniconda3/envs/python39/lib/python3.9/site-packages (1.23.5)\n",
      "Requirement already satisfied: requests in /root/miniconda3/envs/python39/lib/python3.9/site-packages (2.29.0)\n",
      "Requirement already satisfied: matplotlib in /root/miniconda3/envs/python39/lib/python3.9/site-packages (3.7.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from requests) (3.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from requests) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from requests) (1.26.15)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from requests) (2022.12.7)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (4.39.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from matplotlib) (5.12.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
      "Requirement already satisfied: six>=1.5 in /root/miniconda3/envs/python39/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install numpy requests matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "744af584-1202-4c44-a1ac-a6b7b3307908",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data: {\"signature_name\": \"serving_default\", \"instances\": ...  [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]]]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json,numpy\n",
    "import matplotlib.pyplot as plt\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "images = [[[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]], [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]], [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]], [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]], [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], 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[0.00392156862745098], [0.0], [0.0], [0.7803921568627451], [1.0], [0.9686274509803922], [0.22745098039215686], [0.0], [0.6313725490196078], [1.0], [0.9882352941176471], [0.4666666666666667], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]], [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.3843137254901961], [0.6235294117647059], [0.2784313725490196], [0.0], [0.0], [0.26666666666666666], [0.6901960784313725], [0.6431372549019608], [0.22745098039215686], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]]]\n",
    "images = numpy.array(images)\n",
    "\n",
    "image = images[0]\n",
    "plt.figure()\n",
    "plt.imshow(image)\n",
    "\n",
    "data = json.dumps({\"signature_name\": \"serving_default\", \"instances\": images[0:3].tolist()})\n",
    "print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "211d8dff-90f9-414b-929e-8b16d57beefb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8cb3fccUVV0QdfrL1vnfvXu8+ioKCAv31r39V8+bNG932vXv3asOGDUpOTq63DeH7B4Lsg6699tqow8P7ildffdW7/vr222/r6NGjysrKitqWlJQUTZo0SVL0/e+kSZOUmJhYp66goEDnnXeejhw5oo0bN/qeh4Y0+nGi06G+OyjT09MlHf9h1/bJJ59Ikq655hpdc801DX73F198cVrakpqa2uD4tLQ0SVJ5ebk3LPzjDt8ME02HDh0iPisdv6jfUF19w8PL5eabb9bNN99c7zQl/8vlVPhdvydbbpmZmcrKytL+/fu1fft2tWrVKnDb9u7d662z+m6CONnNEX7nz6WTta3277UhixYt0rvvvqtVq1Zp1apVSk5OVp8+fTRs2DBNmTLF26E3xsCBA9WkSRO9+eabOnr0qJo0aaK//vWv6tKli1q3bq38/Hw9+eSTWrNmjaZOneqFcjiwa7vtttt0xx13qLKyst7p1b5RKy0tTY8++qimT5+uhQsXauHChWrZsqUGDBig0aNHa/Lkyd623hhvvPGGioqK9NlnnzVq+o3x3HPPaffu3REH5mEtWrTQ2LFjtXLlSi1dulR33HFH1O8Iut5/9rOfqaqqSiNHjtTy5csVE+PvHGzr1q0yM5WVlUUNrdqC7INOtk8sKyvTvn37lJ2dHXj/e7K6UCikdu3a6cCBA6f9oNpp8PpdueE7BUePHq0WLVo0+Nk2bdqc1rb4batL4eUyePBg7wdVn27durlokqSzZ5kFvcP0ZG8IO1vmL5rT1bbzzz9fb7/9tl5++WWtWbNG69ev15tvvqn169frzjvv1KJFi7RgwYJGfVd8fLyGDBmiVatWqaSkRHl5edqzZ4/3F6xwwL744osNBu///M//6NZbb1VqaqoefPBBXXbZZWrVqpWSk5MVCoV00003adGiRXXu4i0sLFR+fr5WrlypV155RevXr9eKFSu0YsUK/eIXv9CLL76oHj16nHQ+SktLdeWVV2rPnj2aPn26vv/97+uCCy5Qenq6YmNj9fe//12dOnXyfRdx+K7l8vJyDR06tM748M5+2bJl+uUvf6nY2Ng6nwm63idOnKg//elPWrNmjZYtW1bnjPtkwttYamqqCgsLA7XhVPld3mcTp8HrV25urrZs2aIZM2ZowoQJZ7o59WrdurWkr85EowmPC382/N9btmyp97GG+obn5uZKksaPH68f//jHAVp8dgjPf33L7dChQ9q/f7/32bCEhARJ0pEjR6LWhR9/qq1p06ZKTEzUl19+qW3btqlr1651PsPboY4LP2YU/lNheXm5li1bptmzZ+umm27ShAkTTnrAF5afn69Vq1ZpzZo13tlZOFjz8vLUsWNHrV27Vjt27NAHH3ygVq1a1Vk34cfK7rjjDs2cObPOND766KN6p5+RkRHxF7N//vOfmjNnjp555hn98Ic/1Msvv3zSeVi3bp327NmjPn36RH3Mr6Hp12fXrl3e4zb79u3T+vXr6/3szp07tXr1an3nO9/xPZ36jBw5UrNmzdJ3v/tdfe9739PRo0d1ww03NLo+vA8KhUJaunTpaT8ore/SQHgbTUpKUtOmTSV9tW9o6HJCffvf2uMaakftutPh7D2ElzRmzBhJqvd5zrPFkCFDFBMTo02bNulvf/tbnfG7du3S6tWrJcl7BkySd5T7u9/9Lur3PvbYY1GHh5fL8uXLnRz1hYPuVK73RRPesYevc50ovJPr2LFj1A0m2rPBZqZVq1bVGR4fH6+BAwdKkn7/+99HnV74JQanU3x8vKRgy+7rWu5+JSUladasWbroootUU1PjvdqwMWqf1a5Zs0ZxcXERzw7n5+dr9+7duv/++yUdv1Z9ovDBV7S/an3++ed68cUXG92e3Nxc3XbbbZKkTZs2NaomPP36/qz7xBNPNHr6YcuWLVN1dbUuueQS2fGbXKP+u/HGGyU1/ExvUEOGDNHatWt13nnnae7cubrzzjsbXduqVStddNFFOnLkiLdvO53qW6bhfeLgwYO9+wD69u2r1NRU7d+/XytXrqxTU1ZW5j2XXHv/G/4dPvXUU1H/JL9ixQodOHBAaWlpuvjii73hp2O7PKuDd+bMmWrTpo2WL1+uBQsWRD3D2b17t5YsWXIGWveVvLw8TZw4UWam66+/PuLlFceOHdPMmTNVXl6uQYMGadCgQd64GTNmKDU1Va+//roeeOCBiO8sKSnR4sWLo05v/Pjx6tevnzZs2KDp06dHvYZy4MABLV68+LTstHNyciTJu4nldPnXf/1Xpaen63//93915513RhxEvPPOO7r99tslST/5yU8i6sI788cff1ybN2/2hldWVmrBggX13mgXPqJ/4IEH9MYbb0SM+/Wvf60333zz1GfqBKey7Jo3b66EhATt3r3b2/l/3e69996o1zG3bNnindn5uazTo0cPZWdna/PmzXrppZc0YMAA7z4J6at1+eCDD0b8f23h68oPP/ywKioqvOGHDh3S1KlTdejQoTo177zzjp566imVlZXVGffss8/6mo/w9NeuXRvxewu36amnnmrU99QWPqis78bCsPBNRn/+85+/lvs1+vXrp5KSEp1//vn6+c9/rp/+9KeNrg1vn9OnT/eWaW1mpjfffFMvvPCC73Zt3LhR99xzT8SwV199Vf/1X/8lSfrRj37kDU9KStLs2bMlSfPnz4/4i1dlZaXmzp2r3bt3q127dhF/OZ04caLy8vK0c+dOzZs3L2JfuXXrVs2fP1+SNGfOnIgbbcPb9EcffdTgPQcNCnw/tPl/nKih26/r+57/+7//856nyszMtCFDhtjkyZPtyiuvtK5du1ooFLIWLVo0us0NvVzB7KvHM2655Zao4+u7tX7v3r3e83wZGRl25ZVX2oQJE6x58+beM2TRHsd58sknvQfee/ToYcXFxTZkyBALhUL2ox/9qN7lsmPHDu+FHU2aNLFBgwbZpEmTrKCgwHr16uV95+m4Df7BBx/0HsEqKCiwGTNm2IwZM2zLli2N/t765uPZZ5/1HrXq3LmzFRcX24gRIywuLs6k+p/RHj9+vEmy5ORku/zyy23cuHGWk5Nj6enp3mNY0dbxzJkzTZLFxsbasGHDrLi42Lp3726xsbHe8q79qMKpzt+Pf/xjk44/wH/11Vd7y672s94NmTBhgkmy3NxcKy4u9urDTvZ7ra/t9Q0PP1fduXNnu+qqq2zy5Mk2bNgwb31ce+21jWp3beHHfaS6L1Q4cOCA92IESbZjx4469Z988ollZmaaJGvdurUVFhbauHHjLCMjw1q2bGnXXXddnWWwYsUK7/dx6aWX2qRJk2zChAnWqVMnk2QJCQm2atWqRs9D+PeWkJBgI0eOtEmTJlnnzp0tFArZz3/+85M+9lVbSUmJSbLExMRGPbbVp08fk2T33nuvNyz82M1LL70Utaa+30V9+6+PPvrI8vLyTJL94Ac/iHimvqHH93796197v40LLrjAvvOd79jkyZPt8ssvt+zsbJNkCxYsOOk8njhf4RdodOvWzYqLi23o0KHe7yTai0HKy8ttxIgR3jofO3asFRUVefPUtGlTe/vtt+vU1X6BRps2bayoqMjGjh1b7ws0wvr27WvS8Zf+TJkyxWbMmOFrPs/64DUzO3z4sN1zzz02cOBAy8zMtPj4eGvZsqX169fPfvKTn9hrr73W6DZ/XcFrdvx5sUWLFlmvXr0sJSXFkpKSrEuXLnbTTTc1uIG98sorNmrUKEtPT7eUlBTr3bu3PfTQQ2bW8HIpLy+3xYsX2/Dhw61p06YWFxdn2dnZ1qtXL5s9e7Y9//zzEZ8PGrzV1dW2aNEi69atW8RLPcIb/amu382bN9vUqVMtJyfH4uPjLTMz04YPH25/+MMf6v2+8vJyW7hwobVv397i4+MtOzvbiouL7R//+EeD67impsaWLFliffr0saSkJMvMzLSRI0faunXr7LHHHjNJVlxcHFFzKvNXVlZmN954o11wwQXe23zUwDPRJ9q3b59df/31lpeX571RqvZ0TnfwPvHEEzZ9+nTr3r27ZWVlWWJiorVp08bGjBljK1asCPR2pvCzu1LdF2OYffX8dJcuXer9jq1bt9qUKVMsLy/Pa9OsWbNs9+7dUZfBrl277K677rKxY8dau3btLCUlxdLT061r1642e/Zs76CxsSoqKuxXv/qV9ejRw1JSUiwrK8tGjhxpL7zwQqOet67tmmuuMUk2YcKERn3+/vvvr7N8Tnfwmh1/4cqFF17oHWBVVVWdtMbM7L333rOZM2dax44dLSkpyVJSUqx9+/Y2atQoe+CBB6IeTNWn9nytXbvWRowYYRkZGZacnGx9+/a1ZcuW1VtbWVlpv/nNb2zAgAGWlpZmCQkJ1qFDB5szZ45t37693rrPPvvMZs+ebe3bt7eEhARLS0uzgQMH2m9/+9t6nyfftm2bTZ482Vq2bOkdeDR2/ZuZhcy+wbeGAafRddddp0cffVT33Xef5s2bd6abA+AcdVZf4wVOt/fff1/Hjh2LGFZTU6MlS5Zo2bJlSkpKUnFx8RlqHYBvg7P6cSLgdPvVr36lp59+Wr1791br1q117Ngxbd68WZ9++qliY2P1m9/8Ri1btjzTzQRwDiN48a1SVFSkw4cPa+PGjdq0aZOqqqqUnZ2toqIi/du//ZsGDBhwppsI4BzHNV4AABziGi8AAA4RvAAAOETwAgDg0Dl7c9XlMRPPdBMAAKfgxZrlZ7oJXwvOeAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACHCF4AABwieAEAcIjgBQDAIYIXAACH4s50AwDgVITi/O/GrLra/4TM/NcEFJOS4rumprTUd02odzffNZJk77wfqA7HccYLAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBD9E4EnA1CoQA1AY6ba/z3yhPbsb3/6Uj6fFgL3zXZyzf7rqk+eMh3zdkuSE9DQXxydXqgunbvnOaGfMtwxgsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQnSQA31QBOjwIYne+/84OJOlA30rfNcdadvNdk/fL13zXnO3i2uT6rtkx3n9N/BHfJTgNOOMFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiE4SgLNAKC7ed41VVviuqcy/2HfNoU7mu0aS4r/wP09fdij3X/NCW981uw+m+a5JSfK/vCXpwPYM3zXx533puyYjba/vmkM7/bcNp44zXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIfoJAE43WJifZcE6fAgNtP/C+7/PsF/20L+39cvSapO9N+5QnKq/4mFQv6nExPjvybIdCTpgk67fNd8srOZ75oDh5r4rlFcsHnCqeGMFwAAhwheAAAcIngBAHCI4AUAwCGCFwAAhwheAAAcIngBAHCI4AUAwCGCFwAAhwheAAAcIngBAHCI4AUAwCGCFwAAh+idCHWFQv5rLGAvJwF68pHVBKgJ0BtNXLDNw6qqAtX59fH8rr5rEj/3P53Y8gC/B0mlef6XQ0pipe+a7V+c57smJtb/b6imJth5yv7SZP/TqvC/XSSm+e/ZKT4h2G81SM9Y1QcPBZrWuYgzXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIfoJOGbxFXnBUE7PAiiptrJZIJ0eOCqswNJ+vwHg3zXVGT771Ag89143zU1AfcScekVvmv2H2jiu8YOJPivaeq/bfFxwX6r8bFufuMxMf6329Rk/x0rSFJlz/a+a2JefifQtM5FnPECAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RCcJ3ySuOi+IifVdEor1XyNJVuX/Rf9BloPLDg92zfff4cGRC/y3L2mH/w4PvszyXSIL0DeHJCUl+++I4OiuVP8TSvXfCYHV+J/M0bJE/0WSkhP9LwcF6g8l4IoKYNvoJN817V7+GhryDcUZLwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEN0knCqAnQoEFiQN7uHAhxb1QR56bz/GpdiL2jnu+bTSS0DTas62X8nDqkf+98Uq5r4LlF1ov+2VWQFW7cJFf7nKRTgRf9xyQE62gigujrYeUp5hf/OLFTtfzl8Wep/OjU1wTpWaNN/e6A6HMcZLwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEPnbCcJoTj/s2ZVVf4ndJZ3DiBz07643JxAdWWdWviu2d8l0f90zvffOUBMhe8SSVL8Ef8vnq/I8N++qjT/NRbvv0YJATrnkGQBXsCfkXPId01ivP/tdv8h/z1MVFcF6xAlyHJQTIB1Wxago43YAL8HSXuP+l9+zQf2DDStcxFnvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOHTO9k4UqKehAOLa5gWqK7sw23dNZar/3lEqmvg/tqpK9l2iI23910hSdXKAXoMq/dfEHfPfQ4wFPCytSPffvuok/zWhIJ1pJfvvaShUFqxXnsoK/wuwIsH/TB3ck+a7Jj79S981ScnBuqs6dtD/BhXfxP+0mmce9V1zqDTAxi6pS7M9vmu2Z3cMNK1zEWe8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADp2znSQEcXTiJf5rWgV7gXxMgBfclzfzX2OxAV6+X+2/Q4GYKv/TkaTQUf/Tqmrif1rlLap918h/045L8N8RQexB/5tikE4cYlP9//BiYvzPjyRVlsb7rik7lui7Jvaw/20wsbmbTlSCqjyY5Lvm8xr/P4igHT9kJpT5rtkZoHOTcxVnvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA6ds50kHCka4Lum6tp9vmuOftTUd40kJe3xf8wTf9T/dCwmQIcHAd6bbrEBexQIUBYfoGOFmnj/yzsUrG8AVaYFeBl8gOVQneR/OhZgnkJxwV5un5V92HdNl6af+5/QBf5L0uPLfdfEhQJ0tCFJuf5Ldpen+67JTvS/g9hfkeK7RpJ2lmb4rkneeSzQtM5FnPECAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4RPACAOAQwQsAgEMELwAADhG8AAA4dM52kpBZ8onvmr/3b++7JrvrF75rJKlNvwOB6vwqr4r3XbOnNNV3zd4Dab5rJKnqYILvmvjDsb5rauIDdCgQsN8Hy6r0XdOr/We+a5on+X8pfvvkvb5rqi3Y8flNzT70XXP3vo6+a17Y08V3za8u/LPvmqzYRN81klRtwTqZ8KvU/P/uni/NCzStf5S38F3zSmbrQNM6F3HGCwCAQwQvAAAOEbwAADhE8AIA4BDBCwCAQwQvAAAOEbwAADhE8AIA4BDBCwCAQwQvAAAOEbwAADhE8AIA4FDIzNEbvB27PGbimW5Cg2LPO893zeERF/quOXCh/w4F4vr778ChQ5b/l+9LUl4T/9Nqnei/Jlb+f+bVCtZLQmWN/75HNh9t6bvm9U/a+a4576Uk3zXN//Cu7xpJqjl2LFCdCzVrc33XDG/+90DTeveI/84Bdh9L912z71iK75qqKv/7B0mqrPD/G79wtv+Oa1bv/3++a74JOOMFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAheicCAJyVXqxZfqab8LXgjBcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHCJ4AQBwiOAFAMAhghcAAIcIXgAAHAqZmZ3pRgAA8G3BGS8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA4RvAAAOETwAgDgEMELAIBDBC8AAA79f1iyJsv5uxcEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "headers = {\"content-type\": \"application/json\"}\n",
    "json_response = requests.post('http://mnist-202208011.service:8501/v1/models/mnist:predict', data=data, headers=headers)\n",
    "predictions = json.loads(json_response.text)['predictions']\n",
    "\n",
    "def show(idx, title):\n",
    "  plt.figure()\n",
    "  plt.imshow(images[idx].reshape(28,28))\n",
    "  plt.axis('off')\n",
    "  plt.title('\\n\\n{}'.format(title), fontdict={'size': 16})\n",
    "\n",
    "show(0, 'The model thought this was a {}'.format(class_names[numpy.argmax(predictions[0])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18f833d0-7bdb-4156-b5ce-617328814791",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
